{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## 单变量房价预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "sns.set(context=\"notebook\", style=\"whitegrid\", palette=\"dark\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\tensorflow112\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y, data. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x19695d6cf98>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df0 = pd.read_csv('data0.csv', names=['square', 'price'])\n",
    "sns.lmplot('square', 'price', df0, height=6, fit_reg=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47 entries, 0 to 46\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   square  47 non-null     int64\n",
      " 1   price   47 non-null     int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 880.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df0.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 多变量房价预测"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "   square  bedrooms   price\n0    2104         3  399900\n1    1600         3  329900\n2    2400         3  369000\n3    1416         2  232000\n4    3000         4  539900",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>square</th>\n      <th>bedrooms</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2104</td>\n      <td>3</td>\n      <td>399900</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1600</td>\n      <td>3</td>\n      <td>329900</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2400</td>\n      <td>3</td>\n      <td>369000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1416</td>\n      <td>2</td>\n      <td>232000</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3000</td>\n      <td>4</td>\n      <td>539900</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits import mplot3d\n",
    "\n",
    "df1 = pd.read_csv('data1.csv', names=['square', 'bedrooms', 'price'])\n",
    "df1.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x196b38db160>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "# 创建一个 Axes3D object\n",
    "ax = plt.axes(projection='3d')\n",
    "# 设置 3 个坐标轴的名称\n",
    "ax.set_xlabel('square')\n",
    "ax.set_ylabel('bedrooms')\n",
    "ax.set_zlabel('price')\n",
    "# 绘制 3D 散点图\n",
    "ax.scatter3D(df1['square'], df1['bedrooms'], df1['price'], c=df1['price'], cmap='Greens')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据规范化"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "     square  bedrooms     price\n0  0.130010 -0.223675  0.475747\n1 -0.504190 -0.223675 -0.084074\n2  0.502476 -0.223675  0.228626\n3 -0.735723 -1.537767 -0.867025\n4  1.257476  1.090417  1.595389",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>square</th>\n      <th>bedrooms</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.130010</td>\n      <td>-0.223675</td>\n      <td>0.475747</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.504190</td>\n      <td>-0.223675</td>\n      <td>-0.084074</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.502476</td>\n      <td>-0.223675</td>\n      <td>0.228626</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.735723</td>\n      <td>-1.537767</td>\n      <td>-0.867025</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.257476</td>\n      <td>1.090417</td>\n      <td>1.595389</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def normalize_feature(df):\n",
    "    return df.apply(lambda column: (column - column.mean()) / column.std())\n",
    "\n",
    "df = normalize_feature(df1)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "<mpl_toolkits.mplot3d.art3d.Path3DCollection at 0x196b39767f0>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes(projection='3d')\n",
    "ax.set_xlabel('square')\n",
    "ax.set_ylabel('bedrooms')\n",
    "ax.set_zlabel('price')\n",
    "ax.scatter3D(df['square'], df['bedrooms'], df['price'], c=df['price'], cmap='Reds')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47 entries, 0 to 46\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   square    47 non-null     float64\n",
      " 1   bedrooms  47 non-null     float64\n",
      " 2   price     47 non-null     float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 1.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 数据处理：添加 ones 列（x0）"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "ones = pd.DataFrame({'ones': np.ones(len(df))})# ones是n行1列的数据框，表示x0恒为1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "   ones    square  bedrooms     price\n0   1.0  0.130010 -0.223675  0.475747\n1   1.0 -0.504190 -0.223675 -0.084074\n2   1.0  0.502476 -0.223675  0.228626\n3   1.0 -0.735723 -1.537767 -0.867025\n4   1.0  1.257476  1.090417  1.595389",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ones</th>\n      <th>square</th>\n      <th>bedrooms</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.0</td>\n      <td>0.130010</td>\n      <td>-0.223675</td>\n      <td>0.475747</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>-0.504190</td>\n      <td>-0.223675</td>\n      <td>-0.084074</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.0</td>\n      <td>0.502476</td>\n      <td>-0.223675</td>\n      <td>0.228626</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.0</td>\n      <td>-0.735723</td>\n      <td>-1.537767</td>\n      <td>-0.867025</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.0</td>\n      <td>1.257476</td>\n      <td>1.090417</td>\n      <td>1.595389</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([ones, df], axis=1)\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47 entries, 0 to 46\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   ones      47 non-null     float64\n",
      " 1   square    47 non-null     float64\n",
      " 2   bedrooms  47 non-null     float64\n",
      " 3   price     47 non-null     float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 1.6 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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